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In fact, one cannot be sure of 'detection without localisation' without examining the error trials carefully.
For the numerical solution of such problems, a specific approach is required to construct the appropriate difference scheme and examining the error analysis.
By examining the error compositions, we found that the prediction advantages of TAGGING of JF models over that of SF models were related to the lower level of bias before using TAGGING and more stable reduction of bias after using TAGGING.
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Via a variety of examples, we illustrate the efficiency and accuracy of the proposed scheme by examining the errors in different norms and providing the rates of convergence of the method.
A real-world data performance analysis is provided to examine the error rates of the proposed models.
The article[14] examined the error of estimating autocorrelation and the article[15] linked the variation with sampling rate and refined it in the form of mean-square error.
A selected sample of 10 cadastral districts was used to examine the error against the DIBAVOD method and find that the error is relatively low (0.13%).
Using GIS and spatial analysis methods, the aim of this paper is to examine the error associated with using place of residence as opposed to place of injury in injury research.
Finally, we examine the error probability P4. Figure 4 plots the upper bounds of P4 and P1 + P2 + P3 for each decoded bit with E b /N0 = 4 dB.
The aim of this paper is to examine the error associated with using place of residence as opposed to place of injury when identifying injury hotspots and hospital access.
Using highly accurate locational data for both place of injury and place of residence, this analysis examines the error in using place of residence as a proxy for place of injury.
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